MLOps stands for Machine Learning Operations. MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them. MLOps is a collaborative function, often comprising data scientists, devops engineers, and IT.
MLOps is a useful approach for the creation and quality of machine learning and AI solutions. By adopting an MLOps approach, data scientists and machine learning engineers can collaborate and increase the pace of model development and production, by implementing continuous integration and deployment (CI/CD) practices with proper monitoring, validation, and governance of ML models.
Productionizing machine learning is difficult. The machine learning lifecycle consists of many complex components such as data ingest, data prep, model training, model tuning, model deployment, model monitoring, explainability, and much more. It also requires collaboration and hand-offs across teams, from Data Engineering to Data Science to ML Engineering. Naturally, it requires stringent operational rigor to keep all these processes synchronous and working in tandem. MLOps encompasses the experimentation, iteration, and continuous improvement of the machine learning lifecycle.
The primary benefits of MLOps are efficiency, scalability, and risk reduction. Efficiency: MLOps allows data teams to achieve faster model development, deliver higher quality ML models, and faster deployment and production. Scalability: MLOps also enables vast scalability and management where thousands of models can be overseen, controlled, managed, and monitored for continuous integration, continuous delivery, and continuous deployment. Specifically, MLOps provides reproducibility of ML pipelines, enabling more tightly-coupled collaboration across data teams, reducing conflict with devops and IT, and accelerating release velocity. Risk reduction: Machine learning models often need regulatory scrutiny and drift-check, and MLOps enables greater transparency and faster response to such requests and ensures greater compliance with an organization’s or industry’s policies.
Productionizing machine learning is difficult. The machine learning lifecycle consists of many complex components such as data ingest, data prep, model training, model tuning, model deployment, model monitoring, explainability, and much more. It also requires collaboration and hand-offs across teams, from Data Engineering to Data Science to ML Engineering. Naturally, it requires stringent operational rigor to keep all these processes synchronous and working in tandem. MLOps encompasses the experimentation, iteration, and continuous improvement of the machine learning lifecycle.
The best practices for MLOps can be delineated by the stage at which MLOps principles are being applied.
Iteratively explore, share, and prep data for the machine learning lifecycle by creating reproducible, editable, and shareable datasets, tables, and visualizations.
Iteratively transform, aggregate, and de-duplicate data to create refined features. Most importantly, make the features visible and shareable across data teams, leveraging a feature store.
Use popular open source libraries such as scikit-learn and hyperopt to train and improve model performance. As a simpler alternative, use automated machine learning tools such as AutoML to automatically perform trial runs and create reviewable and deployable code.
Track model lineage, model versions, and manage model artifacts and transitions through their lifecycle. Discover, share, and collaborate across ML models with the help of an open source MLOps platform such as MLflow.
Manage the frequency of model refresh, inference request times and similar production-specifics in testing and QA. Use CI/CD tools such as repos and orchestrators (borrowing devops principles) to automate the pre-production pipeline.
Automate permissions and cluster creation to productionize registered models. Enable REST API model endpoints.
Create alerts and automation to take corrective action In case of model drift due to differences in training and inference data.
MLOps is a set of engineering practices specific to machine learning projects that borrow from the more widely-adopted DevOps principles in software engineering. While DevOps brings a rapid, continuously iterative approach to shipping applications, MLOps borrows the same principles to take machine learning models to production. In both cases, the outcome is higher software quality, faster patching and releases, and higher customer satisfaction.
MLflow is an open-source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers MLflow tracking, MLflow Projects, MLflow Models, and Model Registry.
Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost the productivity of data scientists who work on a wide variety of projects, from classical statistics to state-of-the-art deep learning.
Kubeflow is an open-source, machine learning platform, designed to allow machine learning pipelines to orchestrate complicated workflows running on Kubernetes. Kubeflow was based on Google’s internal method to deploy TensorFlow models called TensorFlow Extended.
Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best practices, and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.
FastAPI is a Web framework for developing RESTful APIs in Python. FastAPI is based on Pydantic and type hints to validate, serialize, and deserialize data, and automatically auto-generate OpenAPI documents.
An insurance company wants to improve its cash flow forecasting by better predicting patient charges, using demographic and basic patient health-risk metrics at the time of hospitalization.
I will be using PyCaret in Python for training and developing a machine learning pipeline that will be used as part of our web app. You can use any framework you like as the subsequent steps don’t depend on this.
This tutorial is not focused on building a Flask application. It is only discussed here for completeness. Now that our machine learning pipeline is ready, we need a web application that can read our trained pipeline, to predict new data points.
The back-end of the application is a Python file named app.py. It is built using the Flask framework.
If you are using Windows you will have to install Docker for Wind.Dockerfile is case-sensitive and must be in the project folder with the other project files. A Dockerfile has no extension and can be created using any editor. We have used Visual Studio Code to create it.
After setting up Dockerfile correctly we will write some commands to create a Docker image from this file, but first, we need a service to host that image. In this example, we will be using Microsoft Azure to host our application.
MLOps help ensure that deployed models are well maintained, performing as expected, and not having any adverse effects on the business. This role is crucial in protecting the business from risks due to models that drift over time, or that are deployed but unmaintained or unmonitored.
MLOps topped LinkedIn’s Emerging Jobs ranking, with a recorded growth of 9.8 times in five years.
Machine-learning (ML) models almost always require deployment to a production environment to provide business value. The unfortunate reality is that many models never make it to production, or if they do, the deployment process takes much longer than necessary. Even successfully deployed models will require domain-specific upkeep that can create new engineering and operations challenges.
ML models are software artifacts that need to be deployed. Tracking provenance is critical for deploying any good software and typically handled through version control systems. But, building ML models depends on complex details such as data, model architectures, hyperparameters, and external software. Keeping track of these details is vital, but can be simplified greatly with the right tools, patterns, and practices.
Automation is key to modern DevOps, but it’s more difficult for ML models. In a traditional software application, a continuous integration and continuous delivery (CI/CD) pipeline would pick up some versioned source code for deployment. For an ML application, the pipeline should not only automate training models, but also automate model retraining along with archival of training data and artifacts.
Monitoring software requires good logging and alerting, but there are special considerations to be made for ML applications. All predictions generated by ML models should be logged in such a way that enables traceability back to the model training job. ML applications should also be monitored for invalid predictions or data drift, which may require models to be retrained.
ML models (especially deep learning models) can be harder to test and more computationally expensive than traditional software. It is important to make sure your ML applications function as expected and are resilient to failures. Getting reliability right for ML requires some special considerations around security and testing.
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